期刊名称：Journal of Systemics, Cybernetics and Informatics
印刷版ISSN：1690-4532
电子版ISSN：1690-4524
出版年度：2017
卷号：15
期号：4
页码：9-15
出版社：International Institute of Informatics and Cybernetics
摘要：In dynamical systems, the information flows converge ordiverges in state space and is integrated or communicatedbetween different cells assemblies termed as CFC. This processallows different oscillatory systems to communicate in accuratetime, control and distribute the information flows in cellassemblies. The CF interactions allow the oscillatory rhythms tocommunicate in accurate time, and reintegrate the separatedinformation. The intrinsic brain dynamics inElectroencephalography (EEG) with eye - closed (EC) and eyeopen (EO) during resting states have been investigated to see thechanges in brain complexity i.e. simple visual processing whichare associated with increase in global dimension complexity. Inorder to study these changes in EEG, we have computed thecoupling to see the inhibitory interneurons response and interregionsfunctional connectivity differences between the eyeconditions. We have investigated the fluctuations in EEGactivities in low (delta, theta) and high (alpha) frequency brainoscillations. Coupling strength was estimated using DynamicBayesian inference approach which can effectively detect thephase connectivity subject to the noise within a network of timevarying coupled phase oscillators. Using this approach, we haveseen that delta-alpha and theta-alpha CFC are more dominant inresting state EEG and applicable to multivariate networkoscillator. It shows that alpha phase was dominated by lowfrequency oscillations i.e. delta and theta. These different CFChelp us to investigate complex neuronal brain dynamics at largescale networks. We observed the local interactions at highfrequencies and global interactions at low frequencies. The alphaoscillations are generated from both posterior and anteriororigins whereas the delta oscillations found at posterior regions.
关键词：Electroencephalography (EEG) during resting state;Cross Frequency Coupling; Dynamic Bayesian Inference;Wavelet Transform
